This took me a second to orient to because I kept wanting to jump straight into equations.
Start by framing the problem as a software design exercise: define a clean abstraction for thermal elements using the electrical resistance analogy, then show how they compose into a system. Walk through the physics-to-code mapping for each resistance type, boundary conditions, and heat sources, emphasizing modularity and interface design for integration into larger systems.
Pro tip: Demonstrate awareness of numerical stability and performance trade-offs: for example, radiative resistance is nonlinear (depends on T^4), so you might linearize it or use iterative solvers, and you should mention how your module handles these without breaking the abstraction.
Outline a base class or interface for thermal elements (e.g., ThermalElement) with methods to compute resistance and heat flow, and a ThermalSystem class that assembles elements and solves the network. Emphasize that each element exposes a consistent interface for pluggability.
For conduction, use R = L/(k*A); for convection, R = 1/(h*A); for radiation, use a linearized resistance R = 1/(h_rad*A) where h_rad = εσ(T_s^2 + T_sur^2)(T_s + T_sur). Explain how each is implemented as a subclass or strategy, and note the nonlinearity of radiation.
Represent fixed-temperature boundaries as voltage sources and heat sources as current sources in the analogous circuit. Describe how to incorporate them into the system matrix, and how to handle time-dependent or spatially varying sources via parameters.
Show how to build a thermal network by connecting elements in series/parallel, forming a system of equations (e.g., using nodal analysis). Discuss solver choices (direct vs. iterative) and how to handle nonlinearities (e.g., Newton-Raphson for radiation).
Specify how the module exposes parameters (e.g., material properties, geometry) and results (temperatures, heat fluxes) to a larger composed system. Discuss how it can be nested or coupled with other modules (e.g., via shared boundaries or co-simulation).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.